Hip and Trunk Kinematics Estimation in Gait Through Kalman Filter Using IMU Data at the Ankle

Abstract

The purpose of this paper is to provide a new method of estimating the hip acceleration and trunk posture in the sagittal plane during a walking task using an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). A comparison between these two estimation techniques is also provided. Considering the periodic nature of gait, a modified biomechanical model with Fourier series approximations are utilized as a priori knowledge. Inertial measurement units (IMUs) are placed on the right side of the ankle, hip, and middle of the trunk of twenty recruited participants, as input, a posteriori data, and the ground truth for the model, separately. The results show a better performance of the EKF in estimating the hip acceleration (6.5% error) and the trunk posture (3.12% error). Moreover, both the EKF and the UKF provide low error rates for the trunk posture in comparison to the hip acceleration. This paper provides an inexpensive and novel method to estimate and filter the kinematics of motion for different body locations from a single accurate IMU attached to the ankle considering the periodic nature of gait that can be extended to other activities as well as real-time applications.